code: def test_linear_quantizer_quantization_scheme_setting(config_dict):
      model = nn.Sequential(
          OrderedDict(
              {
                  "conv": nn.Conv2d(1, 20, (3, 3)),
                  "relu": nn.ReLU(),
              }
          )
      )
      config = LinearQuantizerConfig.from_dict(config_dict)
      quantizer = LinearQuantizer(model, config)
  
      def_quantization_scheme = (
          ModuleLinearQuantizerConfig().quantization_scheme.value
      )
      quantization_scheme = quantizer._quantization_scheme.value
      if len(config_dict) == 0:
          assert def_quantization_scheme == quantization_scheme
      else:
          assert quantization_scheme == "affine"
  
file_path: coremltools/coremltools/test/optimize/torch/quantization/test_quantizer.py
imports: from collections import OrderedDict
  import cattrs
  import pytest
  import torch
  import torch.ao.quantization
  import torch.nn
  import torch.nn.intrinsic
  import torch.nn.intrinsic.qat
  import torch.nn.quantized
  import torch.nn.quantized.modules.utils
  from coremltools.optimize.torch.quantization import LinearQuantizer, LinearQuantizerConfig, ModuleLinearQuantizerConfig
label_desc: Use image node_id 5 for calling a global function with example usage: test_linear_quantizer_quantization_scheme_setting(config_dict) without return types